Data sets routinely captured in medical imaging are rapidly increasing in size due to improved geometric resolution and decreased examination times in state of the art imaging modalities, such as, for example, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). One undesired consequence of the increased data set size is increased storage space requirements. A common solution to decrease storage space is to lower the resolution of the stored data. A typical data set with lower resolution is an image volume represented as a number of 2-D slices. Saving space by lowering resolution can translate to merging groups of slices, dramatically reducing the number of slices. This data reduction can be carried out at different points in time, for example in connection with actual examination or later, such as about 6-months later, when the images are less likely to be viewed again.
Unfortunately, saving storage space using lower resolution data sets can reduce image quality. Thus, whenever the images are viewed in reduced form, clinical information can be lost that may be potentially important for patient evaluation and/or treatment. Findings from “full quality” resolution images may be impossible or difficult to evaluate in lower resolution, which can make comparisons to subsequent “new” examinations difficult. In addition, it may be difficult to re-evaluate an earlier questionable or potentially uncertain diagnosis or finding based on the earlier examination image, potentially resulting in false assumptions of information on patient history being used as a basis for a patient's current treatment.
Known two-dimensional (2-D) and three-dimensional (3-D) visualization products provide medical images that can render images from stored electronic data files. The data input used to create the image renderings can be a stack of image slices from a desired imaging modality, for example, a CT or MRI modality. The visualization can convert the image data into an image volume to create renderings that can be displayed on a workstation display.
Slice-by-slice viewing of medical data may be increasingly difficult for the large data sets now provided by imaging modalities, raising issues of information and data overload and clinical feasibility with current radiology staffing levels. See, e.g., Adressing the Coming Radiology Crisis: The Society for Computer Applications in Radiology Transforming the Radiological Interpretation Process (TRIP™) Initiative, Andriole et al., at URL scarnet.net/trip/pdf/TRIP_White_Paper.pdf (November 2003). In some modalities, patient data sets can have large volumes, such as greater than 1 gigabyte, and can even commonly exceed 10's or 100's of gigabytes, hence terabytes of data in a patient multi-dimensional data set is becoming more common.
The diagnostic task of a clinician such as a radiologist can vary patient to patient and, accordingly so can the desired renderings or views of the medical images of the patient. In some visualization systems, a physician uses an interactive workstation that has a data retrieval interface that obtains the medical data for medical image renderings from electronic volume data sets to generate desired medical representations. Image visualizations using the multi-dimensional image data can be carried out using any suitable system such as, for example, PACS (Picture Archiving and Communication System). PACS is a system that receives images from the imaging modalities, stores the data in archives, and distributes the data to radiologists and clinicians for viewing (and can refer to sub portions of these systems).
The lower resolution noted above that has been used to decrease storage space requirements can be particularly relevant for 3-D rendering methods such as Direct Volume Rendering (DVR). In DVR, low resolution can result in jagged contours of anatomical features, negatively impacting diagnostic image quality. As the data sets increase in size, the traditional slice-by-slice viewing can become less efficient and DVR may become a valuable routine clinical tool for generating medical images.
Despite the above, there remains a need for alternate techniques to format multi-dimensional data sets of medical images for storage and/or retrieval.